The subject disclosure relates to artificial intelligence representations, and more specifically, to translation of a graphical representation of domain knowledge associated with an artificial intelligence planning problem.
Automated planning and scheduling is a branch of artificial intelligence (AI) that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots, and unmanned vehicles. Unlike classical control and classification problems, solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory. Planning may be performed such that solutions may be found and evaluated prior to execution; however, any derived solution often needs to be revised. Solutions usually resort to iterative trial and error processes commonly seen in artificial intelligence. These include dynamic programming, reinforcement learning, and combinatorial optimization.
A planning problem generally comprises the following main elements: a finite set of facts, the initial state (a set of facts that are true initially), a finite set of action operators (with precondition and effects), and a goal condition. An action operator maps a state into another state. In the classical planning, the objective is to find a sequence of action operators (or planning action) that, when applied to the initial state, will produce a state that satisfies the goal condition. This sequence of action operators is called a plan.
Plan recognition is the problem of recognizing the plans and the goals of an agent given a set of observations. There exist a number of different approaches to the plan recognition problem including the use of SAT solvers and planning where the domain theory is given as an input as well as the use of techniques that assume a plan library is given as an input. Plan recognition continues to be an important problem to study as it has many practical applications such as assisted cognition, computer games, and network monitoring.
For example, as described in Gregor Behnke et al., “Integrating Ontologies and Planning for Cognitive Systems” (Proc. Of the 28th Int. Workshop on Description Logics (2015)), “patterns and mechanisms that suitably link planning domains and interrelated knowledge in an ontology” are described. In Gregor Behnke et al., “an approach for using an ontology as the central source of domain knowledge for a cognitive system” is described. However, Gregor Behnke et al. does not present solutions to encoding domain knowledge that do not require specialized knowledge of the ontology.
As another example, as described in Rabia Jilani, “ASCoL: A Tool for Improving Automatic Planning Domain Model Acquisition” (Advances in Artificial Intelligence, 2015), “AI planning requires domain models.” Rabia Jilani introduces “ASCoL, a tool that exploits graph analysis for automatically identifying static relations, in order to enhance planning domain models.” However, Rabia Jilani does not address receipt of dynamic information, or lack of plan traces in the received information. Furthermore, Rabia Jilani does not present solutions to encoding domain knowledge that do not require specialized knowledge of the plan traces.
Furthermore, many other conventional approaches to plan recognition also require specialized knowledge of artificial intelligence description languages in order to be useful. It may be relatively difficult for a person having specialized domain knowledge, but lacking specialized artificial intelligence training, to fully utilize artificial intelligence planning. Furthermore, a lack of accurately encoded domain knowledge in the artificial intelligence description language can prohibit the use of artificial intelligence planning for plan recognition problems in general.